Journal Description
Electronics
Electronics
is an international, peer-reviewed, open access journal on the science of electronics and its applications published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE) is affiliated with Electronics and their members receive a discount on article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, and other databases.
- Journal Rank: JCR - Q2(Electrical and Electronic Engineering) CiteScore - Q2 (Electrical and Electronic Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.6 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Electronics include: Magnetism, Signals, Network and Software.
Impact Factor:
2.9 (2022);
5-Year Impact Factor:
2.9 (2022)
Latest Articles
Efficient Gaussian Process Calculations Using Chebyshev Nodes and Fast Fourier Transform
Electronics 2024, 13(11), 2136; https://doi.org/10.3390/electronics13112136 (registering DOI) - 30 May 2024
Abstract
Gaussian processes have gained popularity in contemporary solutions for mathematical modeling problems, particularly in cases involving complex and challenging-to-model scenarios or instances with a general lack of data. Therefore, they often serve as generative models for data, for example, in classification problems. However,
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Gaussian processes have gained popularity in contemporary solutions for mathematical modeling problems, particularly in cases involving complex and challenging-to-model scenarios or instances with a general lack of data. Therefore, they often serve as generative models for data, for example, in classification problems. However, a common problem in the application of Gaussian processes is their computational complexity. To address this challenge, sparse methods are frequently employed, involving a reduction in the computational domain. In this study, we propose an innovative computational approach for Gaussian processes. Our method revolves around selecting a computation domain based on Chebyshev nodes, with the optimal number of nodes determined by minimizing the degree of the Chebyshev series, while ensuring meaningful coefficients derived from function values at the Chebyshev nodes with fast Fourier transform. This approach not only facilitates a reduction in computation time but also provides a means to reconstruct the original function using the functional series. We conducted experiments using two computational methods for Gaussian processes: Markov chain Monte Carlo and integrated nested Laplace approximation. The results demonstrate a significant reduction in computation time, thereby motivating further development of the proposed algorithm.
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(This article belongs to the Section Systems & Control Engineering)
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Enhancing Edge-Assisted Federated Learning with Asynchronous Aggregation and Cluster Pairing
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Xiaobao Sha, Wenjian Sun, Xiang Liu, Yang Luo and Chunbo Luo
Electronics 2024, 13(11), 2135; https://doi.org/10.3390/electronics13112135 (registering DOI) - 30 May 2024
Abstract
Federated learning (FL) is widely regarded as highly promising because it enables the collaborative training of high-performance machine learning models among a large number of clients while preserving data privacy by keeping the data local. However, many existing FL frameworks have a two-layered
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Federated learning (FL) is widely regarded as highly promising because it enables the collaborative training of high-performance machine learning models among a large number of clients while preserving data privacy by keeping the data local. However, many existing FL frameworks have a two-layered architecture, thus requiring the frequent exchange of large-scale model parameters between clients and remote cloud servers over often unstable networks and resulting in significant communication overhead and latency. To address this issue, we propose to introduce edge servers between the clients and the cloud server to assist in aggregating local models, thus combining asynchronous client–edge model aggregation with synchronous edge–cloud model aggregation. By leveraging the clients’ idle time to accelerate training, the proposed framework can achieve faster convergence and reduce the amount of communication traffic. To make full use of the grouping properties inherent in three-layer FL, we propose a similarity matching strategy between edges and clients, thus improving the effect of asynchronous training. We further propose to introduce model-contrastive learning into the loss function and personalize the clients’ local models to address the potential learning issues resulting from asynchronous local training in order to further improve the convergence speed. Extensive experiments confirm that our method exhibits significant improvements in model accuracy and convergence speed when compared with other state-of-the-art federated learning architectures.
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(This article belongs to the Special Issue Recent Advances in Collaborative Systems and Control in the Industrial Sector)
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Combined Use of Python and DIgSILENT PowerFactory to Analyse Power Systems with Large Amounts of Variable Renewable Generation
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Javier Jiménez-Ruiz, Andrés Honrubia-Escribano and Emilio Gómez-Lázaro
Electronics 2024, 13(11), 2134; https://doi.org/10.3390/electronics13112134 (registering DOI) - 30 May 2024
Abstract
Over the last decade considerable efforts have been made to reduce greenhouse gas emissions, leading to the significant development and implementation of renewable energy plants across all power systems in the world. Wind energy has consolidated its position as one of the two
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Over the last decade considerable efforts have been made to reduce greenhouse gas emissions, leading to the significant development and implementation of renewable energy plants across all power systems in the world. Wind energy has consolidated its position as one of the two key energy sources (in conjunction with solar photovoltaics) to achieve completely green power systems. Integrating wind energy into power systems is a more complicated task compared to traditional generation systems, as wind energy relies on a variable energy source characterised by high variability. Several tools currently exist to simulate the effect of wind energy generation in power systems, but they often lack the versatility demanded by researchers. This paper analyses how both Python 3.11 and DIgSILENT PowerFactory 2024 can be used synergistically to assess the implementation of wind power plants, highlighting how the use of these two tools combined can be of great interest for both researchers and grid operators.
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(This article belongs to the Special Issue Planning, Operation and Control of Power Systems with Large Amounts of Variable Renewable Generation)
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Failure Mechanism Information-Assisted Multi-Domain Adversarial Transfer Fault Diagnosis Model for Rolling Bearings under Variable Operating Conditions
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Zhidan Zhong, Zhihui Zhang, Yunhao Cui, Xinghui Xie and Wenlu Hao
Electronics 2024, 13(11), 2133; https://doi.org/10.3390/electronics13112133 (registering DOI) - 30 May 2024
Abstract
Deep transfer learning tackles the challenge of fault diagnosis in rolling bearings across variable operating conditions, which is pivotal for intelligent bearing health management. Traditional transfer learning may not be able to adapt to the specific characteristics of the target domain, especially in
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Deep transfer learning tackles the challenge of fault diagnosis in rolling bearings across variable operating conditions, which is pivotal for intelligent bearing health management. Traditional transfer learning may not be able to adapt to the specific characteristics of the target domain, especially in the case of variable working conditions or lack of annotated data for the target domain. This may lead to unstable training results or negative transfer of the neural network. This paper proposes a new method for enhancing unsupervised domain adaptation in bearing fault diagnosis, aimed at providing robust fault diagnosis for rolling bearings under varying operating conditions. It incorporates bearing fault finite element simulation data into the domain adversarial network, guiding adversarial training using fault evolution mechanisms. The algorithm establishes global and subdomain classifiers, with simulation signals replacing label predictions for target data in the subdomain, ensuring minimal information transfer. By reconstructing the loss function, we can extract the common features of the same type bearing under different conditions and enhance the domain antagonism robustness. The proposed method is validated using two sets of testbed data as target domains. The results demonstrate that the method can adequately adapt the deep feature distributions of the model and experimental domains, thereby improving the accuracy of fault diagnosis in unsupervised cross-domain scenarios.
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(This article belongs to the Topic Predictive Analytics and Fault Diagnosis of Machines with Machine Learning Techniques)
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Video Detection Method Based on Temporal and Spatial Foundations for Accurate Verification of Authenticity
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Chin-Yuan Lin, Jen-Chun Lee, Shuenn-Jyi Wang, Chung-Shi Chiang and Chao-Lung Chou
Electronics 2024, 13(11), 2132; https://doi.org/10.3390/electronics13112132 (registering DOI) - 30 May 2024
Abstract
With the rapid development of deepfake technology, it is finding applications in virtual movie production and entertainment. However, its potential for malicious use, such as generating false information, fake news, or synthetic pornography, poses significant threats to national and social security. Various research
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With the rapid development of deepfake technology, it is finding applications in virtual movie production and entertainment. However, its potential for malicious use, such as generating false information, fake news, or synthetic pornography, poses significant threats to national and social security. Various research disciplines are actively engaged in developing deepfake video detection technologies to mitigate the risks associated with malicious deepfake content. Therefore, the importance of deepfake video detection technology cannot be overemphasized. This study addresses the challenge posed by images in nonexistent datasets by analyzing deepfake video detection methods. Using temporal and spatial detection techniques and employing 68 facial landmarks for alignment and feature extraction, this research integrates the attention-guided data augmentation (AGDA) strategy to enhance generalization capabilities. The detection performance is evaluated on four datasets: UADFV, FaceForensics++, Celeb-DF, and DFDC, with superior results compared to alternative approaches. To evaluate the study’s ability to accurately discriminate authenticity, detection experiments are conducted on both genuine and deepfake videos synthesized using the DeepFaceLab and FakeApp frameworks. The experimental results show better performance in detecting deepfake videos than other methods compared.
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(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)
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An Accurate Cooperative Localization Algorithm Based on RSS Model and Error Correction in Wireless Sensor Networks
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Bo Chang, Xinrong Zhang and Haiyi Bian
Electronics 2024, 13(11), 2131; https://doi.org/10.3390/electronics13112131 (registering DOI) - 30 May 2024
Abstract
Aiming at the problem that there is a big contradiction between accuracy and calculation and cost based on the RSSI positioning algorithm, an accurate and effective cooperative positioning algorithm is proposed in combination with error correction and refinement measures in each stage of
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Aiming at the problem that there is a big contradiction between accuracy and calculation and cost based on the RSSI positioning algorithm, an accurate and effective cooperative positioning algorithm is proposed in combination with error correction and refinement measures in each stage of positioning. At the ranging stage, the RSSI measurement value is converted to distance by wireless channel modeling and the dynamic acquisition of the power attenuation factor. Then, the ranging correction is carried out by using the known anchor node ranging error information. The Taylor series expansion least-square iterative refinement algorithm is implemented in the position optimization stage, and satisfactory positioning accuracy is obtained. The idea of cooperative positioning is introduced to upgrade the nodes that meet the requirements and are upgraded to anchor nodes and participate in the positioning of other nodes to improve the positioning coverage and positioning accuracy. The experimental results show that the localization effect of this algorithm is close to that of the Taylor series expansion algorithm based on coordinates but far higher than that of the basic least-squares localization algorithm. The positioning accuracy can be improved rapidly with the decrease in the distance measurement error.
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(This article belongs to the Special Issue Featured Advances in Real-Time Networks)
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Developing Different Test Conditions to Verify the Robustness and Versatility of Robotic Arms Controlled by Evolutionary Algorithms
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Roland Szabo
Electronics 2024, 13(11), 2130; https://doi.org/10.3390/electronics13112130 (registering DOI) - 29 May 2024
Abstract
In this paper, different test cases where robotic arms are tested will be presented. A robotic arm is tested for the gravity effects that can be observed on it. The other robotic arm is tested for how much precision it has by using
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In this paper, different test cases where robotic arms are tested will be presented. A robotic arm is tested for the gravity effects that can be observed on it. The other robotic arm is tested for how much precision it has by using it to learn to write. The other robotic arm is tested on how well it can function as a solar tracker and how precisely it can function as an energy harvester. On the basis of these tests, the robotic arm’s mechanical structure, electronics, and software are put to the test. The software is based on evolutionary software that implements genetic algorithms. The entire command system is also ported to FPGAs (to hardware) to increase speed and response time.
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(This article belongs to the Section Industrial Electronics)
Open AccessArticle
Rapid Beam Tracking Using Power Measurement for Terahertz Communications
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Xiaodan He, Changming Zhang, Chi Lu and Xianbin Yu
Electronics 2024, 13(11), 2129; https://doi.org/10.3390/electronics13112129 (registering DOI) - 29 May 2024
Abstract
With abundant bandwidth resources, terahertz communications are considered one of the key technologies to meet the requirement for high data-rate transmission in the future. In order to compensate for the severe propagation loss of terahertz communications, directional antennas with high gain and narrow
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With abundant bandwidth resources, terahertz communications are considered one of the key technologies to meet the requirement for high data-rate transmission in the future. In order to compensate for the severe propagation loss of terahertz communications, directional antennas with high gain and narrow beams are expected to be adopted, making beam tracking significant for robust communications. In this paper, a tracking method based on power measurement is proposed, consisting of beam status monitoring, recognition of the deviation direction, and movement toward the optimal angle. By observing the change in the received signal power, beam misalignment is first checked, and whether the misalignment is out of tracking range is also determined. Then, the deviation direction is recognized by comparing the received power variations in the candidate directions, and the beam angle is adjusted accordingly until it reaches the optimal angle. With a small scanning range, the deviation direction is recognized in a short duration, allowing for rapid beam tracking. Numerical results indicate that the alignment error is competitively low and stable in the proposed beam tracking method, and its technical superiority is particularly dominant in situations involving variable motion at high speeds.
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(This article belongs to the Special Issue Millimeter-Wave and Terahertz Technologies for Wireless Communications)
Open AccessArticle
Motion Coordination of Multiple Autonomous Mobile Robots under Hard and Soft Constraints
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Spyridon Anogiatis, Panagiotis S. Trakas and Charalampos P. Bechlioulis
Electronics 2024, 13(11), 2128; https://doi.org/10.3390/electronics13112128 (registering DOI) - 29 May 2024
Abstract
This paper presents a distributed approach to the motion control problem for a platoon of unicycle robots moving through an unknown environment filled with static obstacles under multiple hard and soft operational constraints. Each robot has an onboard camera to determine its relative
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This paper presents a distributed approach to the motion control problem for a platoon of unicycle robots moving through an unknown environment filled with static obstacles under multiple hard and soft operational constraints. Each robot has an onboard camera to determine its relative position in relation to its predecessor and proximity sensors to detect and avoid nearby obstascles. Moreover, no robot apart from the leader can independently localize itself within the given workspace. To overcome this limitation, we propose a novel distributed control protocol for each robot of the fleet, utilizing the Adaptive Performance Control (APC) methodology. By utilizing the APC approach to address input constraints via the on-line modification of the error specifications, we ensure that each follower effectively tracks its predecessor without encountering collisions with obstacles, while simultaneously maintaining visual contact with its preceding robot, thus ensuring the inter-robot visual connectivity. Finally, extensive simulation results are presented to demonstrate the effectiveness of the presented control system along with a real-time experiment conducted on an actual robotic system to validate the feasibility of the proposed approach in real-world scenarios.
Full article
(This article belongs to the Special Issue Path Planning for Mobile Robots, 2nd Edition)
Open AccessArticle
Parameter Optimization of Josephson Parametric Amplifiers Using a Heuristic Search Algorithm for Axion Haloscope Search
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Younggeun Kim, Junu Jeong, Sungwoo Youn, Sungjae Bae, Arjan F. van Loo, Yasunobu Nakamura, Sergey Uchaikin and Yannis K. Semertzidis
Electronics 2024, 13(11), 2127; https://doi.org/10.3390/electronics13112127 (registering DOI) - 29 May 2024
Abstract
The cavity haloscope is among the most widely adopted experimental platforms designed to detect dark matter axions with its principle relying on the conversion of axions into microwave photons in the presence of a strong magnetic field. The Josephson parametric amplifier (JPA), known
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The cavity haloscope is among the most widely adopted experimental platforms designed to detect dark matter axions with its principle relying on the conversion of axions into microwave photons in the presence of a strong magnetic field. The Josephson parametric amplifier (JPA), known for its quantum-limited noise characteristics, has been incorporated into the detection system to capture the weakly interacting axion signals. However, the performance of the JPA can be influenced by its environment, leading to the potential unreliability of a predefined parameter set obtained in a specific laboratory setting. Furthermore, conducting a broadband search requires the consecutive characterization of the amplifier across different tuning frequencies. To ensure more reliable measurements, we utilize the Nelder–Mead technique as a numerical search method to dynamically determine the optimal operating conditions. This heuristic search algorithm explores the multidimensional parameter space of the JPA, optimizing critical characteristics such as gain and noise temperature to maximize signal-to-noise ratios for a given experimental setup. Our study presents a comprehensive analysis of the properties of a flux-driven JPA to demonstrate the effectiveness of the algorithm. This approach contributes to ongoing efforts in axion dark matter research by offering an efficient method to enhance axion detection sensitivity through the optimized utilization of JPAs.
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(This article belongs to the Special Issue Recent Advances and Applications in New Detectors)
Open AccessArticle
Vulnerability Assessment and Topology Reconstruction of Task Chains in UAV Networks
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Qingfeng Yue, Jinglei Li, Zijia Huang, Xiaoyu Xie and Qinghai Yang
Electronics 2024, 13(11), 2126; https://doi.org/10.3390/electronics13112126 (registering DOI) - 29 May 2024
Abstract
With the increasing complexity of environments and the diversity of task chains, individual unmanned aerial vehicles (UAVs) often struggle to satisfy the demands of task chains, including load capacity improvement, information perception, and information procession. In complex task chains involving various UAVs, such
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With the increasing complexity of environments and the diversity of task chains, individual unmanned aerial vehicles (UAVs) often struggle to satisfy the demands of task chains, including load capacity improvement, information perception, and information procession. In complex task chains involving various UAVs, such as area reconnaissance and fire rescue, any attack on critical UAVs can greatly disrupt the execution of the entire task chain by causing equipment damage or connectivity disruption. To ensure network resilience post attack, identifying vulnerable nodes in the UAV network becomes crucial. In this paper, a Vulnerability-based Topology Reconstruction Mechanism (VUTRM) is proposed to rank the importance of nodes in task chains and formulate a topology reconstruction. It consists of two parts: the first part is a Multi-metric Node Vulnerability Assessment Algorithm (MENVAL) used to rank the importance of nodes in task chains, and the second part is a Node Importance-based Topology Reconstruction Algorithm (NITRA) used to reconstruct the UAV network with the obtained node ranking. Finally, simulations carried out with simulation software demonstrate that our proposed method accurately identifies network vulnerabilities and promptly implements effective reconstruction measures to minimize network damage.
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(This article belongs to the Special Issue Data Privacy and Cybersecurity in Mobile Crowdsensing)
Open AccessArticle
A High-Performance Non-Indexed Text Search System
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Binh Kieu-Do-Nguyen, Tuan-Kiet Dang, Nguyen The Binh, Cuong Pham-Quoc, Huynh Phuc Nghi, Ngoc-Thinh Tran, Katsumi Inoue, Cong-Kha Pham and Trong-Thuc Hoang
Electronics 2024, 13(11), 2125; https://doi.org/10.3390/electronics13112125 (registering DOI) - 29 May 2024
Abstract
Full-text search has a wide range of applications, including tracking systems, computer vision, and natural language processing. Standard methods usually implement a two-phase procedure: indexing and retrieving, with the retrieval performance entirely dependent on the index efficiency. In most cases, the more powerful
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Full-text search has a wide range of applications, including tracking systems, computer vision, and natural language processing. Standard methods usually implement a two-phase procedure: indexing and retrieving, with the retrieval performance entirely dependent on the index efficiency. In most cases, the more powerful the index algorithm, the more memory and processing time are required. The amount of time and memory required to index a collection of documents is proportional to its overall size. In this paper, we propose a full-text search hardware implementation without the indexing phase, thus removing the time and memory requirements for indexing. Additionally, we propose an efficient design to leverage the parallel architecture of High Bandwidth Memory (HBM). To our knowledge, few (if not zero) researchers have integrated their full-text search system with an effective data access control on HBM. The functionality of the proposed system is verified on the Xilinx Alveo U50 Field-Programmable Gate Array (FPGA). The experimental results show that our system achieved a throughput of 8 Gigabytes per second, about 6697× speed-up compared to other software-based approaches.
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(This article belongs to the Section Microelectronics)
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Aggregation Equivalence Method for Direct-Drive Wind Farms Based on the Excitation–Response Relationship
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Gangui Yan, Yupeng Wang, Yuxing Fan, Cheng Yang and Lin Yue
Electronics 2024, 13(11), 2124; https://doi.org/10.3390/electronics13112124 (registering DOI) - 29 May 2024
Abstract
The grid interconnections for direct-drive wind farms have triggered multiple new sub-synchronous oscillation events, which can prevent the power system from operating safely and stably. However, the excessively high order of the detailed model for large-scale wind farms with multiple direct-drive permanent magnet
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The grid interconnections for direct-drive wind farms have triggered multiple new sub-synchronous oscillation events, which can prevent the power system from operating safely and stably. However, the excessively high order of the detailed model for large-scale wind farms with multiple direct-drive permanent magnet synchronous generators (PMSGs) connected to power systems poses a challenge when investigating small disturbance stability and instability mechanisms. This study establishes a model of the grid-connected PMSG system based on the voltage/power excitation–response relationship to describe the dynamic characteristics of the port of the PMSG, and the analysis of active and reactive response characteristics of PMSG lays the foundation for model simplification. Based on the unit model, a direct-drive wind farm aggregation equivalence method based on the excitation–response relationship is proposed. The equivalent model obtained by this method is suitable for the small disturbance stability analysis of direct-drive wind farms grid connected system, with good accuracy. The simulation verified the effectiveness of the aggregation model.
Full article
(This article belongs to the Special Issue Advances in Power System Dynamics, Stability, Control and Dispatch with Large-Scale Renewable Energy Penetrated)
Open AccessArticle
Deep Pre-Training Transformers for Scientific Paper Representation
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Jihong Wang, Zhiguang Yang and Zhanglin Cheng
Electronics 2024, 13(11), 2123; https://doi.org/10.3390/electronics13112123 (registering DOI) - 29 May 2024
Abstract
In the age of scholarly big data, efficiently navigating and analyzing the vast corpus of scientific literature is a significant challenge. This paper introduces a specialized pre-trained BERT-based language model, termed SPBERT, which enhances natural language processing tasks specifically tailored to the domain
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In the age of scholarly big data, efficiently navigating and analyzing the vast corpus of scientific literature is a significant challenge. This paper introduces a specialized pre-trained BERT-based language model, termed SPBERT, which enhances natural language processing tasks specifically tailored to the domain of scientific paper analysis. Our method employs a novel neural network embedding technique that leverages textual components, such as keywords, titles, abstracts, and full texts, to represent papers in a vector space. By integrating recent advancements in text representation and unsupervised feature aggregation, SPBERT offers a sophisticated approach to encode essential information implicitly, thereby enhancing paper classification and literature retrieval tasks. We applied our method to several real-world academic datasets, demonstrating notable improvements over existing methods. The findings suggest that SPBERT not only provides a more effective representation of scientific papers but also facilitates a deeper understanding of large-scale academic data, paving the way for more informed and accurate scholarly analysis.
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(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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Secure Encryption of Biomedical Images Based on Arneodo Chaotic System with the Lowest Fractional-Order Value
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Berkay Emin, Akif Akgul, Fahrettin Horasan, Abdullah Gokyildirim, Haris Calgan and Christos Volos
Electronics 2024, 13(11), 2122; https://doi.org/10.3390/electronics13112122 (registering DOI) - 29 May 2024
Abstract
Fractional-order (FO) chaotic systems exhibit richer and more complex dynamic behaviors compared to integer-order ones. This inherent richness and complexity enhance the security of FO chaotic systems against various attacks in image cryptosystems. In the present study, a comprehensive examination of the dynamical
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Fractional-order (FO) chaotic systems exhibit richer and more complex dynamic behaviors compared to integer-order ones. This inherent richness and complexity enhance the security of FO chaotic systems against various attacks in image cryptosystems. In the present study, a comprehensive examination of the dynamical characteristics of the fractional-order Arneodo (FOAR) system with cubic nonlinearity is conducted. This investigation involves the analysis of phase planes, bifurcation diagrams, Lyapunov exponential spectra, and spectral entropy. Numerical studies show that the Arneodo chaotic system exhibits chaotic behavior when the lowest fractional-order (FO) value is set to 0.55. In this context, the aim is to securely encrypt biomedical images based on the Arneodo chaotic system with the lowest FO value using the Nvidia Jetson Nano development board. However, though the lowest FO system offers enhanced security in biomedical image encryption due to its richer dynamic behaviors, it necessitates careful consideration of the trade-off between high memory requirements and increasing complexity in encryption algorithms. Within the scope of the study, a novel random number generator (RNG) is designed using the FOAR chaotic system. The randomness of the random numbers is proven by using internationally accepted NIST 800-22 and ENT test suites. A biomedical image encryption application is developed using pseudo-random numbers. The images obtained as a result of the application are evaluated with tests such as histogram, correlation, differential attack, and entropy analyses. As a result of the study, it has been shown that encryption and decryption of biomedical images can be successfully performed on a mobile Nvidia Jetson Nano development card in a secure and fast manner.
Full article
(This article belongs to the Special Issue Nonlinear Circuits and Systems: Latest Advances and Prospects)
Open AccessArticle
Few-Shot Image Classification Based on Swin Transformer + CSAM + EMD
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Huadong Sun, Pengyi Zhang, Xu Zhang and Xiaowei Han
Electronics 2024, 13(11), 2121; https://doi.org/10.3390/electronics13112121 (registering DOI) - 29 May 2024
Abstract
In few-shot image classification (FSIC), the feature extraction module of the traditional convolutional neural networks is often constrained by the local nature of the convolutional kernel. As a result, it becomes challenging to handle global information and long-distance dependencies effectively. In order to
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In few-shot image classification (FSIC), the feature extraction module of the traditional convolutional neural networks is often constrained by the local nature of the convolutional kernel. As a result, it becomes challenging to handle global information and long-distance dependencies effectively. In order to address this problem, an innovative FSIC method is proposed in this paper, which is the integration of Swin Transformer and CSAM and Earth Mover’s Distance (EMD) technology (STCE). We utilize the Swin Transformer network for image feature extraction, and perform CSAM attention mechanism feature weighting on the output feature map, while we adopt the EMD algorithm to generate the optimal matching flow between the structural units, minimizing the matching cost. This approach allows for a more precise representation of the classification distance between images. We have conducted numerous experiments to validate the effectiveness of our algorithm. On three commonly used few-shot datasets, namely mini-ImageNet, tiered-ImageNet, and FC100, the accuracy of one-shot and five-shot has reached the state of the art (SOTA) in the FSIC; the mini-ImageNet achieves an accuracy of 98.65 ± 0.1% for one-shot and 99.6 ± 0.2% for five-shot tasks, while tiered ImageNet has an accuracy of 91.6 ± 0.1% for one-shot tasks and 96.55 ± 0.27% for five-shot tasks. For FC100, the accuracy is 64.1 ± 0.3% for one-shot tasks and 79.8 ± 0.69% for five-shot tasks. On two commonly used few-shot datasets, namely CUB, CIFAR-FS, CUB achieves an accuracy of 83.1 ± 0.4% for one-shot and 92.88 ± 0.4% for five-shot tasks, while CIFAR-FS achieves an accuracy of 86.95 ± 0.2% for one-shot and 94 ± 0.4% for five-shot tasks.
Full article
(This article belongs to the Topic Computer Vision and Image Processing, 2nd Edition)
Open AccessArticle
Rolling Bearing Residual Useful Life Prediction Model Based on the Particle Swarm Optimization-Optimized Fusion of Convolutional Neural Network and Bidirectional Long–Short-Term Memory–Multihead Self-Attention
by
Jianzhong Yang, Xinggang Zhang, Song Liu, Ximing Yang and Shangfang Li
Electronics 2024, 13(11), 2120; https://doi.org/10.3390/electronics13112120 (registering DOI) - 29 May 2024
Abstract
In the context of predicting the remaining useful life (RUL) of rolling bearings, many models often encounter challenges in identifying the starting point of the degradation stage, and the accuracy of predictions is not high. Accordingly, this paper proposes a technique that utilizes
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In the context of predicting the remaining useful life (RUL) of rolling bearings, many models often encounter challenges in identifying the starting point of the degradation stage, and the accuracy of predictions is not high. Accordingly, this paper proposes a technique that utilizes particle swarm optimization (PSO) in combination with the fusing of a one-dimensional convolutional neural network (CNN) and a multihead self-attention (MHSA) bidirectional long short-term memory (BiLSTM) network called PSO-CNN-BiLSTM-MHSA. Initially, the original signals undergo correlation signal processing to calculate the features, such as standard deviation, variance, and kurtosis, to help identify the beginning location of the rolling bearing degradation stage. A new dataset is constructed with similar degradation trend features. Subsequently, the particle swarm optimization (PSO) algorithm is employed to find the optimal values of important hyperparameters in the model. Then, a convolutional neural network (CNN) is utilized to extract the deterioration features of rolling bearings in order to predict their remaining lifespan. The degradation features are inputted into the BiLSTM-MHSA network to facilitate the learning process and estimate the remaining lifespan of rolling bearings. Finally, the degradation features are converted to the remaining usable life (RUL) via the fully connected layer. The XJTU-SY rolling bearing accelerated life experimental dataset was used to verify the effectiveness of the proposed method by k-fold cross-validation. After comparing our model to the CNN-LSTM network model and other models, we found that our model can achieve reductions in mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of 9.27%, 6.76%, and 2.35%, respectively. Therefore, the experimental results demonstrate the model’s accuracy in forecasting remaining lifetime and support its ability to forecast breakdowns.
Full article
(This article belongs to the Special Issue Fault Detection Technology Based on Deep Learning)
Open AccessArticle
Research on the Quantitative Assessment Method of HVDC Transmission Line Failure Risk during Wildfire Disaster
by
Bo Zhou, Xinwei Sun, Yunyang Xu and Wei Wei
Electronics 2024, 13(11), 2119; https://doi.org/10.3390/electronics13112119 (registering DOI) - 29 May 2024
Abstract
It is increasingly important to effectively predict the failure of HVDC transmission lines caused by wildfire disasters. On the basis of comprehensively considering the distribution of fire points, the characteristics of wildfire propagation, and the failure factors of the transmission line, a method
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It is increasingly important to effectively predict the failure of HVDC transmission lines caused by wildfire disasters. On the basis of comprehensively considering the distribution of fire points, the characteristics of wildfire propagation, and the failure factors of the transmission line, a method for calculating the probability of failure in HVDC transmission lines during wildfire disasters is proposed to quantify the risk of HVDC transmission line failures caused by wildfire disasters. Using the ArcGIS 10.7. platform, the study examined the quantity of fire points within the buffer zone of each HVDC transmission line from 2001 to 2022. The results indicate significant variations in the number of fire incidents in the buffer zones of various transmission lines. Notably, there has been a noticeable increase in the number of fire incidents along several HVDC transmission lines, including Xizhe, Baihetan-Jiangsu, Baihetan-Zhejiang, and Fufeng, in recent years. Based on the number of fire points in the buffer zone obtained through ArcGIS processing and the proposed failure probability calculation model, six HVDC hydropower transmission channels in the Sichuan Province were analyzed. At the same time, the proposed probability calculation model was simplified, and a corresponding linear evaluation index was introduced. The regression analysis results indicate that the proposed index can effectively assess the failure risk of HVDC transmission lines during wildfire disasters.
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(This article belongs to the Special Issue The Hybrid AC-DC Power System Coordinated Control and Operation Technology, Volume II)
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A State-Feedback Control Strategy Based on Grey Fast Finite-Time Sliding Mode Control for an H-Bridge Inverter with LC Filter Output
by
En-Chih Chang, Rong-Ching Wu, Heidi H. Chang and Chun-An Cheng
Electronics 2024, 13(11), 2118; https://doi.org/10.3390/electronics13112118 (registering DOI) - 29 May 2024
Abstract
An H-bridge inverter with LC (inductor-capacitor) filter output allows the conversion of DC (direct current) power to AC (alternating current) power that has been used in a variety of applications, such as uninterruptible power supplies, AC motor drives, and renewable energy source systems.
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An H-bridge inverter with LC (inductor-capacitor) filter output allows the conversion of DC (direct current) power to AC (alternating current) power that has been used in a variety of applications, such as uninterruptible power supplies, AC motor drives, and renewable energy source systems. The fast finite-time sliding mode control (FFTSMC) features acceleration of the system state towards the equilibrium position as well as conserving insensitivity against internal parameter fluctuations as well as external load disturbances falling within the predetermined bounds. However, the FFTSMC would potentially witness chattering or steady-state errors as indefinite margins come to be exaggerated or underestimated. The chattering in the sliding mode control practice is oscillatory defective behavior. It induces inefficient operation, higher switching power losses in the transistor circuits, as well as saturated actuators, thus impairing the inverter’s output energy efficiency and raising harmonic distortion. Therefore, this paper presents the H-bridge inverter with LC filter output, which is controlled by a grey prediction fast finite-time sliding mode trajectory tracking. A more highly accurate grey prediction model based on the centered approximation methodology is deployed to vanish the chattering as well as steady-state errors. Taking into account the union of grey prediction and FFTSMC, a feedback-controlled H-bridge inverter with LC filter output allows attaining a highly efficient as well as quality sine-wave output voltage. The presented state-feedback control strategy is robust, less complex, attains more rapid convergence, and is highly accurate. The design process, computer simulation, as well as experimental results of the proposed state-feedback control strategy established that the H-bridge inverter with LC filter output has the capability to exhibit fast dynamic response time as well as good steady-state tracking behavior of the output voltage under step-loading changes and nonlinear loading conditions.
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(This article belongs to the Special Issue Innovative Technologies in Power Converters, Volume II)
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Open AccessArticle
To (US)Be or Not to (US)Be: Discovering Malicious USB Peripherals through Neural Network-Driven Power Analysis
by
Koffi Anderson Koffi, Christos Smiliotopoulos, Constantinos Kolias and Georgios Kambourakis
Electronics 2024, 13(11), 2117; https://doi.org/10.3390/electronics13112117 (registering DOI) - 29 May 2024
Abstract
Nowadays, The Universal Serial Bus (USB) is one of the most adopted communication standards. However, the ubiquity of this technology has attracted the interest of attackers. This situation is alarming, considering that the USB protocol has penetrated even into critical infrastructures. Unfortunately, the
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Nowadays, The Universal Serial Bus (USB) is one of the most adopted communication standards. However, the ubiquity of this technology has attracted the interest of attackers. This situation is alarming, considering that the USB protocol has penetrated even into critical infrastructures. Unfortunately, the majority of the contemporary security detection and prevention mechanisms against USB-specific attacks work at the application layer of the USB protocol stack and, therefore, can only provide partial protection, assuming that the host is not itself compromised. Toward this end, we propose a USB authentication system designed to identify (and possibly block) heterogeneous USB-based attacks directly from the physical layer. Empirical observations demonstrate that any extraneous/malicious activity initiated by malicious/compromised USB peripherals tends to consume additional electrical power. Driven by this observation, our proposed solution is based on the analysis of the USB power consumption patterns. Valuable power readings can easily be obtained directly by the power lines of the USB connector with low-cost, off-the-shelf equipment. Our experiments demonstrate the ability to effectively distinguish benign from malicious USB devices, as well as USB peripherals from each other, relying on the power side channel. At the core of our analysis lies an Autoencoder model that handles the feature extraction process; this process is paired with a long short-term memory (LSTM) and a convolutional neural network (CNN) model for detecting malicious peripherals. We meticulously evaluated the effectiveness of our approach and compared its effectiveness against various other shallow machine learning (ML) methods. The results indicate that the proposed scheme can identify USB devices as benign or malicious/counterfeit with a perfect F1-score.
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(This article belongs to the Special Issue Cyber Attacks: Threats and Security Solutions)
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